We propose to construct a realistic statistical model of lung cancer risk and progression that will make it possible to relate current trends in lung cancer incidence and mortality to past trends in smoking in the US population. We depart from existing approaches by having the model include genetic and behavioral determinants of susceptibility, progression of the disease from precursor lesions through early localized tumors to disseminated disease, detection by various modalities, and medical intervention. Using model estimates as a foundation, we intend to predict mortality reduction caused by primary prevention, and early-detection and intervention programs, under different scenarios. This includes utilization of genetic indicators of susceptibility to lung cancer to define the highest-risk subgroups of the high-risk behavior population (smokers). To allow for uncertainty in the various sources of data we will develop parameter estimation techniques using simulation and Bayesian hierarchical modeling approaches. Along with developing new methodology, we will apply our techniques to a variety of data sets available to us, which will allow calibration and validation of the model. To investigate and develop lung cancer susceptibility, we will use tobacco impact estimates developed at the University of California at San Diego, as well as case-control genetic data on lung cancer maintained by the Epidemiology Department at MD Anderson Cancer Center. To investigate incidence of lung cancer we will use public registry data of the SEER type. For disease progression, early detection and intervention, we will use data from the NCI lung cancer chest X-ray screening studies, and the recent ELCAP CT-scan screening study developed at Weill Medical College of Cornell University. The team assembled for the proposed work includes researchers at Rice University, MD Anderson Cancer Center, Weill Medical College of Cornell University and University of California at San Diego, whose documented expertise spans population studies, modeling of natural history of cancer, impact of screening, Bayesian techniques, genetic epidemiology, statistical genetics and risks analysis of smoking. Data used and generated by the project, as well as software, will be made available to CISNET members.